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CBDAS: A cloud-based data analytics system for predicting health conditions and preventing critical care events

human-computer-interaction
computer-networks-and-communications
distributed-and-parallel-computing
emerging-technologies
mobile-and-ubiquitous-computing

Abstract

The rapid expansion of health data from electronic health records, wearable devices, and public sources presents new opportunities for data-driven preventive care. In this study, we present a Cloud-Based Data Analytics System (CBDAS) for the early prediction and prevention of critical health events in patients with chronic diseases. The CBDAS integrates and harmonizes diverse demographic, clinical, behavioral, and sensor-derived data within a secure, scalable cloud infrastructure. Advanced machine learning models are developed and validated using large-scale national (NHANES) and ICU (MIMIC-IV) datasets, achieving high accuracy (up to 98.7%) and robust discrimination (ROC AUC up to 0.999) for risk stratification. The system enables real-time monitoring, generates personalized alerts and recommendations, and supports patients, physicians, and healthcare organizations in proactive health management. Experimental results demonstrate the CBDAS’s ability to deliver actionable insights for both community-based and acute care settings, with high sensitivity for detecting high-risk individuals while reducing false positives. These findings highlight the potential of cloud-based analytics to transform chronic disease management, support value-based care, and optimize resource utilization in modern healthcare systems.
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